license: mit
language:
- en
base_model:
- google-t5/t5-small
GenAlpha Translator
This model is fine-tuned from T5 to translate between English and GenAlpha language.
Model description
GenAlpha Translator is a fine-tuned version of the T5 model that specializes in bidirectional translation between standard English and "GenAlpha" language. It uses a text-to-text approach where both translation directions are supported through prompt-based inputs.
The model handles two types of translation tasks:
- English to GenAlpha:
translate English to GenAlpha: [English text] - GenAlpha to English:
translate GenAlpha to English: [GenAlpha text]
Intended uses & limitations
Intended uses
This model is designed for:
- Translating standard English text into GenAlpha language
- Translating GenAlpha language back to standard English
- Understanding and generating content in the GenAlpha dialect
Limitations
- The model's translation quality depends on the quality and diversity of the training dataset
- Performance may vary for specialized terminology or complex language constructs
- The model has a maximum sequence length of 128 tokens for both input and output
Training and evaluation data
The model was trained on a custom dataset of English-GenAlpha pairs. Each training instance contains parallel texts in both languages. The dataset was split into training (90%) and validation (10%) sets during the fine-tuning process.
Training procedure
Training hyperparameters
The model was fine-tuned using the following hyperparameters:
- Learning rate: 5e-5
- Batch size: 8
- Training epochs: 3
- Optimizer: AdamW
- Learning rate scheduler: Linear with warmup
- Maximum sequence length: 128
- Random seed: 42
- FP16 mixed precision: Enabled (when CUDA is available)
Framework versions
- Transformers: Required by the training script
- PyTorch: Required for model training
- Datasets: Used for dataset handling
- TensorBoard: Used for training visualization
Model performance
Performance metrics are available through TensorBoard logs generated during training. The model was evaluated using translation loss on the validation set, with checkpoints saved based on the best evaluation loss.
Ethical considerations
When using this model, consider:
- The potential biases present in the training data may be reflected in translations
- The model should not be used to generate misleading or harmful content
- Cultural nuances and context may be lost in translation
Technical information
Model architecture
This model is a fine-tuned version of the T5 encoder-decoder architecture, specifically designed for bidirectional translation tasks. It preserves the original model architecture while specializing in English-GenAlpha translation.
Input format
The model expects text in the following format:
- For English to GenAlpha:
translate English to GenAlpha: [English text] - For GenAlpha to English:
translate GenAlpha to English: [GenAlpha text]
Output format
The model outputs the translated text in the target language.
How to use
from transformers import T5ForConditionalGeneration, T5Tokenizer
# Load model & tokenizer
model_path = "path/to/saved/model"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
# Translate English to GenAlpha
english_text = "Hello, how are you today?"
input_text = f"translate English to GenAlpha: {english_text}"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=128)
genalpha_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(genalpha_text)
# Translate GenAlpha to English
genalpha_text = "Example GenAlpha text here"
input_text = f"translate GenAlpha to English: {genalpha_text}"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=128)
english_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(english_text)